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We present an approach for flux analysis in process algebra models ofbiological systems. We perceive flux as the flow of resources in stochasticsimulations. We resort to an established correspondence between eventstructures, a broadly recognised model of concurrency, and state transitions ofprocess models, seen as Petri nets. We show that we can this way extract thecausal resource dependencies in simulations between individual statetransitions as partial orders of events. We propose transformations on thepartial orders that provide means for further analysis, and introduce asoftware tool, which implements these ideas. By means of an example of apublished model of the Rho GTP-binding proteins, we argue that this approachcan provide the substitute for flux analysis techniques on ordinarydifferential equation models within the stochastic setting of process algebras.

Flux Analysis in Process Models via Causality